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Identification Of The Initial Transient In Discrete-Event Simulation Output Using The Kalman Filter, Mark A. Gallagher

Theses and Dissertations

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. Algorithms for determining the appropriate initial-data truncation point for univariate and multivariate output are proposed. The techniques entail averaging across replications and estimating a steady-state output model in a state-space framework. Using the estimated model, Multiple Model Adaptive Estimation (MMAE), which uses Kalman filters with different parameter vectors, is applied. Based on the filters' residuals, the conditional probabilities of each filter's specific parameter vector being correct are determined. The MMAE parameter estimates are the probabilistic- weighted average of the filters' assumed parameter vectors. The estimated …